SciELO - Scientific Electronic Library Online

 
vol.6 issue3Robust RM-KNN Filters with Different Influence Functions for Removal of Impulsive Noise in Digital ImagesPredictive Control Based on an Auto-Regressive Neuro-Fuzzy Model Applied to the Steam Generator Startup Process at a Fossil Power Plant author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.6 n.3 México Jan./Mar. 2003

 

Artículo

 

Noisy Binary Texture Recognition Using the Coordinated Cluster Transform

 

Reconocimiento de Texturas Binarias Ruidosas Usando la Transformada de Cúmulos Coordinados

 

Evguenii Kurmyshev, Francisco Cuevas and Raúl Sánchez

 

Centro de Investigaciones en Óptica, A.C. Apartado Postal 1–948 León Guanajuato, México. E–mails: kev@foton.cio.mx ; fjcuevas@foton.cio.mx ; sanchez@foton.cio.mx

 

Article received on May 6, 2002
Accepted on March 14, 2003

 

Abstract

In this paper a technique using the coordinated cluster representation (CCR) is examined for recognition of binary computer generated and natural texture images corrupted by additive noise. A normalized local property histogram of the CCR is used as a unique feature vector. The ability of the descriptor to capture spatial statistical features of an image is exploited. The evaluation criteria is the recognition performance using a simple minimum distance classifier for recognition purposes. The experimental results indicate that the proposed technique is efficient for recognition of textures deteriorated by high level additive noise. Textures under test run through periodic up to random ones.

Keywords: Pattern recognition, binary texture analysis, image representation, coordinated clusters.

 

Resumen

En este artículo se estudia una técnica, basada en la representación de imágenes por cúmulos coordinados (RICC), para el reconocimiento de imágenes binarias tanto de texturas naturales como aquellas generadas por computadora, las cuales fueron corrompidas por un ruido aditivo. El histograma normalizado de RICC es usado como vector único de características de la imagen. Se explota la habilidad del descriptor de captar las características estadísticas espaciales de una imagen. Como un criterio de evaluación usamos la eficiencia de reconocimiento usando un clasificador simple de distancia mínima para el reconocimiento. Se muestra que la técnica propuesta es eficiente para el reconocimiento de texturas deterioradas por el ruido aditivo. Se prueba la eficiencia del método en un rango amplio de texturas, siendo éstas desde puramente periódicas hasta completamente aleatorias.

Palabras clave: Reconocimiento de patrones, análisis de texturas binarias, representación de imágenes, cúmulos coordinados.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

Acknowledgments

This work was supported by CONACYT under the grant No. 31168–A. Francisco Cuevas thanks the Centro de Investigaciones en Óptica, A. C, CONACYT, and Centro de Investigación en Computación of the Instituto Politécnico Nacional of México for the support.

 

References

Azencott R. and Wang J.P., "Texture classification using windowed Fourier filters", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No.2, pp.148–157, 1997.        [ Links ]

Berry J.R. and Goutsias J., "A Comparison of Matrix Texture Features Using a Maximum Likelihood Texture Classifier" in Visual Communication and Image Processing IV, Proc. SPIE, 1199, pp. 305–316, 1989.        [ Links ]

Brodatz P., Textures: A Photographic Album for Artists and Designers, Dover Publications, New York, 1966.        [ Links ]

Chellappa R. and Chatterjee S., "Classification of textures using Gaussian Markov random fields", IEEE Trans. Acoust. Speech Signal Process., Vol. 33, pp. 959–963, 1987.        [ Links ]

Chen Ch., Pau L.F. and Wang P. S. P., Handbook of Pattern Recognition & Computer Vision, World Scientific, Singapore, 1996.        [ Links ]

Chetverikov D., "Texture analysis using feature–based pair wise interaction maps", Pattern Recognition, Vol. 32, No.3, pp. 487–498, 1999.        [ Links ]

Duda R.O., Hart P.E. and Stork D.G., "Nonparametric techniques" in Pattern Classification, Wiley–Interscience, John Wiley & Sons Inc., New York, pp. 161–213, 2001.        [ Links ]

Elfadel I.M. and Picard R.W., "Gibbs random fields, cooccurrences and texture modeling", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No. 1, pp. 24–31, 1994.        [ Links ]

Fukunaga K., Introduction to Statistical Pattern Recognition, Academic Press, New York, 1990.        [ Links ]

Goon A. and Rolland J.P., "Texture classification based on comparison of second–order statistics I: 2P–PDF estimation and distance measure", J. Opt. Soc. Am. A, Vol. 16, No.7, pp. 1566–1574, 1999.        [ Links ]

Haralick R.M., "Statistical and structural approaches to texture" in Proc. IEEE, Vol. 67, No.5, pp. 786–804, 1979.        [ Links ]

Kahil M. and Bongard M., Pattern Recognition, Spartan Books, Washington, D.C., 1970.        [ Links ]

Kurmyshev E.V. and Cervantes M., "A quasi–statistical approach to digital image representation", Rev. Mex. Fis., Vol. 42, No.1, pp. 104–116, 1996.        [ Links ]

Kurmyshev E.V. and Soto R., "Digital Pattern Recognition in the coordinated cluster representation" in NORSIG 96 (1996 IEEE NORDIC SIGNA L PROCESSING SYMPOSIUM), Espoo, Finland, pp. 463–466, 1996.        [ Links ]

Kurmyshev E.V. and Sánchez–Yáñez R.E., "Texture Classification based on Image representation by Coordinated Clusters" in Proceedings Image and Vision Computing New Zealand 2001, Dunedin, NZ, pp. 213–217, 2001.        [ Links ]

Liu S. Sh. and Jernigan M.E., "Texture analysis and discrimination in additive noise", Computer Vision, Graphics and Image Processing, Vol. 49, No.1, pp. 52–69, 1990.        [ Links ]

López, D. and Castro, M.J., "Neural–based classification of blocks from documents", International Conference on Neural Networks 2002, Madrid, España.        [ Links ]

Ohanian P.P. and Dubes R.C., "Performance evaluation for four classes of textural features", Pattern Recognition, Vol. 25, No. 8, pp. 819–833, 1992.        [ Links ]

Ojala T., Pietikainen M. and Harwood D., "A comparative study of texture measures with classification based on feature distributions", Pattern Recognition, Vol. 29, No. 1, pp. 51 –59, 1996.        [ Links ]

Randen T. and Husøy J.H., "Filtering for Texture Classification: A Comparative Study", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 4, 1999, pp. 291–310.        [ Links ]

Sánchez–Yáñez R.E., Kurmyshev E.V. and Cuevas F.J., "A Framework for Texture Classification using the Coordinated Clusters Representation", Pattern Recognition Letters, Vol. 24, pp. 21–31, 2003.        [ Links ]

Servín M. and Cuevas F.J., "A new kind of neural network based on radial basis functions", Rev. Mex. Fis., Vol. 39, No.2, pp. 235–249, 1993.        [ Links ]

Soh L.K. and Tsatsoulis C, "Texture analysis of SAR sea ice imagery using gray level co–occurrence matrices", IEEE Transactions on Geosciences and Remote Sensing, Vol. 37, No.2, pp. 780–787, 1999.        [ Links ]

Thomson M.G.A. and Foster D.H., "Role of second– and third–order statistics in the discriminability of natural images", J. Opt. Soc. Am. A, Vol. 14, No.9, pp. 2081–2090, 1997.        [ Links ]

Tuceryan M. and Jain A.K. "Texture Analysis" in Handbook of Pattern Recognition and Computer Vision, ( C.H. Chen, L.F. Pau and P.S.P Wang Eds.), World Scientific Publishing Company, Singapore, pp. 235–276, 1993.        [ Links ]

Turner M.R., "Texture discrimination by Gabor functions", Biol. Cyber. Vol. 55, pp. 71–82, 1986.        [ Links ]

Valkealahti K. and Oja E., "Reduced multidimensional co–occurrence histograms in texture analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, pp. 90–95, 1998.        [ Links ]

Wang, L. and He D.C., "Texture classification using texture spectrum", Pattern Recognition, Vol. 23, pp. 905–910, 1990.        [ Links ]

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License